Color Deconvolution via Spatial Smoothness Regularization
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Solution Overview
Problem
Current color deconvolution methods for multicolor and multichannel images in histopathology fail to maintain spatial continuity and smoothness of tissue structures due to independent pixel-wise unmixing, leading to artifacts like 'holes and cracks' in unmixed images.
Innovation Solution
The method introduces a spatial smoothness constraint that considers neighboring pixels, using a cost function that combines observed data and smoothness regularization, allowing for simultaneous unmixing of entire images or patches, which ensures structural continuity and smooth varying stain concentrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If independent pixel-wise unmixing is used, then computational efficiency is improved, but spatial continuity and smoothness of tissue structures deteriorate
Solution Approach 1:
The patent merges the independent pixel-wise unmixing operations into a simultaneous unmixing approach that processes entire images or patches together. This is achieved by formulating the unmixing problem as a global optimization problem where the cost function includes both data fidelity terms and spatial smoothness regularization terms, allowing the solution at one pixel to depend on the solutions at neighboring pixels.
Solution Approach 2:
The patent introduces spatial smoothness constraint that creates feedback between neighboring pixels. The cost function includes a regularization term that penalizes large differences between neighboring pixel solutions, effectively creating a feedback mechanism where the unmixing result at each pixel is adjusted based on the results at its neighbors to maintain spatial continuity.
2Speed
If traditional color deconvolution methods are used, then processing speed is improved, but image quality with holes and cracks deteriorates
Solution Approach 1:
The patent applies preliminary spatial smoothness constraint before the final unmixing operation. By incorporating the spatial continuity information into the cost function formulation and solving the optimization problem simultaneously across the entire image or patch, the method prevents the formation of holes and cracks during the unmixing process itself, rather than correcting them as a separate post-processing step.
3Device complexity
If pixel solutions are solved independently, then algorithm complexity is reduced, but structural continuity of biological structures deteriorates
Solution Approach 1:
The patent adds a spatial dimension to the unmixing problem by incorporating neighborhood information into the cost function. Instead of solving only the pixel-wise unmixing problem, the method simultaneously solves for the unmixing coefficients of multiple neighboring pixels, effectively moving from a one-dimensional pixel-by-pixel approach to a multi-dimensional global optimization approach that preserves structural continuity.
Data Source
AI summary
The present disclosure involves a computer-implemented unmixing algorithm that employs a least-square method involving image patches, and a computer-implemented unmixing or color deconvolution method that incorporates spatial smoothness and structure continuity constraints, for example, into a neighborhood graph regularizer, such as using a graph to enforce the pixel value similarities for those pixels in the same neighborhood.